Light-independent regulation of algal photoprotection by CO2 availability

Photosynthetic algae have evolved mechanisms to cope with suboptimal light and CO2 conditions. When light energy exceeds CO2 fixation capacity, Chlamydomonas reinhardtii activates photoprotection, mediated by LHCSR1/3 and PSBS, and the CO2 Concentrating Mechanism (CCM). How light and CO2 signals converge to regulate these processes remains unclear. Here, we show that excess light activates photoprotection- and CCM-related genes by altering intracellular CO2 concentrations and that depletion of CO2 drives these responses, even in total darkness. High CO2 levels, derived from respiration or impaired photosynthetic fixation, repress LHCSR3/CCM genes while stabilizing the LHCSR1 protein. Finally, we show that the CCM regulator CIA5 also regulates photoprotection, controlling LHCSR3 and PSBS transcript accumulation while inhibiting LHCSR1 protein accumulation. This work has allowed us to dissect the effect of CO2 and light on CCM and photoprotection, demonstrating that light often indirectly affects these processes by impacting intracellular CO2 levels.

mixotrophic growth under HL. As a result, we calculate the percentage of reactions that show a significant difference in flux between WT and mutants only under mixotrophic HL condition (Supplementary Dataset 3). We found the highest percentage of reactions with significant change for mixotrophic HL condition, but no change under autotrophic LL and HL, for the following pathways: N-Glycan biosynthesis (100%, 26 reactions) and protein synthesis (100%, 1 reaction), followed by tyrosine metabolism (57%, 4 reactions) and valine, leucine and isoleucine degradation (52%, 17 reactions). In addition, pathways like fatty acid biosynthesis (47%, 29 reactions), nitrogen metabolism (45%, 10 reactions), photosynthesis (44%, 4 reactions) as well as starch and sucrose metabolism (40%, 6 reactions) and glycerolipid metabolism (40%, 138 reactions) fall in the highest ranked pathways with respect to percentage of reactions with significant change for mixotrophic HL. These observed changes in fluxes may be explained by transcriptional reprogramming that affect downstream enzyme abundances who support the flux changes. As demonstrated in our experimental validation, CO2 can serve as a signal for these transcriptional reprogramming. In addition, other mechanisms related to allosteric regulation of reaction rates cannot be excluded.
Prompted by these findings, we interrogated whether or not changes in flux are associated with changes in the internal CO2 concentration. Since FBA cannot be used to predict concentrations of metabolites, we used a technique employed in the design of metabolic engineering strategies to modulate the production (and hence concentration) of a metabolite of interest. This technique entails insertion of a synthetic 'demand' reaction for the metabolite of interest, which exports the metabolite out of the network. In our case, we inserted a demand reaction for CO2 from the chloroplast to the environment, and used its maximum flux, at the specific condition associated with strain-specific-growth constraints, as a proxy for intracellular CO2. We then inspected the condition-specific flux through the added demand reaction for different combinations of CO2 and acetate uptake rates (Supplementary Fig. 2). We observed the same flux pattern for the CO2 demand reaction with varying rates of CO2 uptake from the environment across all strains under autotrophic LL and HL. We hypothesized that under LL more CO2 can accumulate because of slow carbon fixation in comparison to HL conditions, where CO2 fixation is faster. In support of this hypothesis, we found that all strains showed larger flux through the CO2 demand reaction, as a proxy for the internal CO2 levels, under LL than HL conditions when the CO2 uptake rates were larger than 0.2 mmol/gDW/h (Supplementary Fig. 2a-c). Under mixotrophic HL conditions, with the assumption of no change in CO2 uptake from the environment and a decrease of at least 10% in acetate uptake for both mutants in comparison to WT, we found that both the icl and dum11 mutants showed smaller flux through the CO2 demand reaction, i.e. lower internal CO2 concentration than what was observed for WT ( Supplementary Fig. 2d-f). Furthermore, the same pattern holds with the assumption that CO2 uptake under HL is at least as high as under LL and acetate uptake rates are below 0.3 mmol/gDW/h. In contrast, only few combinations of CO2 and acetate uptake rates for which the mutant strains showed CO2 demand that is similar under autotrophic LL and mixotrophic HL conditions, but larger than the CO2 demand in the WT under auxotrophic LL conditions. Therefore, we concluded that larger CO2 demand flux under autotrophic LL than HL conditions for each strain can be observed with the assumptions that: (i) the CO2 uptake was not affected by the mutation, (ii) CO2 uptake is the same for phototrophic HL and mixotrophic HL, (iii) CO2 uptake under HL is at least as high as under LL and (iv) the acetate uptake rate is low (i.e., below 0.3 mmol/gDW/h) for the mutants (as indicated in Fig. 2c and f). Moreover, under mixotrophic HL conditions, both mutants exhibited CO2 demand rates that were smaller than those under autotrophic LL conditions. In contrast, the WT showed a marked increase in the CO2 demand flux under mixotrophic HL conditions in comparison to autotrophic LL and HL, indicating higher internal CO2 concentrations in the presence of acetate. In conclusion, genome-scale metabolic modelling supports the hypothesis that there are changes in the internal CO2 concentration under autotrophic and mixotrophic growth conditions at different light intensities. These changes are congruent with the changes in the accumulation of LHCSR3 transcripts under the different media conditions and in the WT and mutant cells.

Condition and strain-specific metabolic models
Simulations of different strain, , and conditions, }, are based on the genome-scale metabolic network reconstruction iCre1355 of Chlamydomonas reinhardtii metabolism 2 . The reconstruction provides the underlying structure of the metabolic reactions captured in the stoichiometric matrix, , where rows correspond to metabolites and columns denote reactions. Each entry in the stoichiometric matrix indicates the molarity with which a metabolite is consumed (negative value) or produced (positive value) by the respective reaction. In addition, condition-specific lower and upper bounds on reaction flux, , for autotrophic (LL, HL) and mixotrophic (HL + acetate) growth are provided with the model. To obtain models for the mutants icl and dum11 we used the gene-protein-reaction rules, provided along the network reconstruction, to identify reactions related to knocked-out genes Cre06.g282800 and CreMt.g000300, respectively. Gene Cre06.g282800 relates to reaction isocitrate lyase and therefore, flux through this reaction is blocked in the simulations of icl. For the mutant dum11 the knocked-out gene CreMt.g000300 was not part of the model. However, it is known that this mutant shows no activity of respiratory complex III, therefore the corresponding model reaction was blocked in the simulation of dum11.
The strain and condition-specific simulations, together with constraint-based modeling approaches were used to investigate steady state flux distributions, . First, we used the WT model under autotrophic conditions to obtain estimates for photon uptake rates under LL and HL conditions, later used as constraints in the mutant models and for the WT model under HL + acetate condition. Therefore, we take generation time ( ) of Chlamydomonas WT under LL and HL measured by Bonente et al. 3 and converted them into growth rates ( ) assuming that . The respective growth rate was used to constrain the WT model under LL and HL conditions. To estimate photon uptake in units mmol gDW -1 h -1 under low and high light conditions, we found the minimum photon uptake rate that supports the condition-specific WT growth rate ( ) under LL and HL, respectively (Eq. 1). The resulting photon uptake rates were used as constraints for the simulation of mutants as well as under HL mixotrophic growth for the WT (were no measured growth rates were available). The following is the linear program that we solve: (1) Next, we used the observation that mutants cannot grow on acetate in darkness to find acetate uptake rates that allow simulation of no growth in darkness for both mutants. Acetate uptake for both mutants were reduced by 90% in comparison to the WT rate (0.2 mmol gDW -1 h -1 in mutants and 2 mmol gDW -1 h -1 in WT), since this rate is the minimum uptake rate for which no growth was simulated in darkness.

Flux balance analysis
To simulate maximal growth rates for WT under HL + acetate as well as icl and dum11 under LL, HL and HL + acetate respectively, we applied flux balance analysis (FBA; 5,6 ) We found maximal growth rates (Eq. 2) by using the model biomass reaction for mixotrophic and photoautotrophic growth and the respective light constraints. Moreover, acetate uptake for mutant models under HL + acetate was set to 0.2 mmol gDW -1 h -1 , the minimum acetate uptake rate for which no growth was simulated in darkness. To this end, we used the following program: (2) .

Flux ranges
The solution of the linear programming problem in Eq. (2), above, is the maximum growth, i.e. flux value of the strain and condition-specific biomass reaction, . Flux variability analysis (FVA) allows determining the minimum and the maximum value of flux that a given reaction can carry while ensuring maximum flux through the biomass reaction 7 . These values can be obtained by solving the following linear program for a given reaction . The flux through the conditionspecific biomass reaction was set to 99% of the optimum to avoid numerical instabilities. To conduct FVA, we solved the following linear programs: Moreover, we sample 5000 feasible steady-state flux distributions from the flux cone of the strain and condition-specific models by applying the function gpSampler from the COBRA toolbox 8 . Here, too, biomass was set to 99% of its optimum.

Non-overlapping flux ranges
Flux ranges between wild type and mutants are considered to not overlap for condition if (1) or , i.e. the minimum flux obtained from FVA (see Eq. 3) in a mutant is larger than the maximum flux obtained for the wild type or the minimum flux in the wild type is larger than the maximum flux for the mutant; i.e. there is no intersection between the flux ranges; (2) minimum flux for WT and mutants is greater than 0.01 mmol gDW -1 h -1 , to avoid considering reactions with low absolute flux; and (3) in line with differential expression analysis, where one considers genes differentially expressed above a preselected fold-change (e.g. of at least 2, in nominal values), for the flux ranges that do not overlap, we use a threshold on the relative difference between the lower bounds of at least 5% (we used 5% to be less restrictive) to filter for cases were flux ranges are close to each other; this condition is meant to remove any numerical artifacts.

Maximize CO2 demand
We introduce a demand reaction for CO2 in the chloroplast and maximize its flux given constraints described in the linear program in Eq. (4), which include the fixation of condition-specific growth as well as uptake rates of CO2 and acetate. The obtained flux through CO2 demand will serve as a proxy for internal CO2 concentration in the chloroplast: (4) Supplementary Fig. 1: Effect of carbon availability on the photosynthetic properties of WT, icl and icl-C cells. a relative photosynthetic electron transfer rETR measured at 336 µmol photons m -2 s -1 and b qE of WT, icl and icl-C cells exposed to 600 µmol photons m -2 s -1 in HSM for 4h; sparged with air (labelled as "air"); sparged with air and supplemented with 10 mM sodium acetate (labelled as "acet); sparged with air enriched with 5% CO2 (labelled as "CO2"), (n = 3 biological samples, mean ± s.d.). The statistical analyses (two-way ANOVA with Tukey's multiple comparisons tests) of a and b are shown in the graph; * = P value < 0.05, ***=P value <0.001. Exact p-values can be found at the Source Data file. c Raw data of in vivo chlorophyll fluorescence (normalized to the highest Fm') for WT, icl and icl-C. Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 21 (labelled as "L1") and 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. Shown are one representative trace of three biological replicates. bubbled with air and supplemented with 10 mM sodium acetate (labelled "acet); bubbled with air enriched with 5% CO2 (labelled "CO2"). After sampling for the LL conditions, light intensity was increased to 600 µmol photons m -2 s -1 (HL); samples were taken after 1h. Accumulation of mRNA of selected CCM genes at the indicated conditions normalized to WT LL ctrl. Please note that these data derive from analyses of the RNA samples of the experiment described in Fig. 1 (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons of acetate and CO2 conditions to air are based on ANOVA Dunnett's multiple comparisons test of log10 transformed mRNA data as indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001, ns, not significant). Exact p-values can be found at the Source Data file.     Supplementary Fig. 7: Complementation of cia5 mutant. a Immunoblot analyses of CIA5-FLAG and ATPB (loading control) from whole cell extracts of cia5-C. The first two lanes, were loaded with cia5-C samples from the experiment presented in Fig. 3b (pre-acclimated in LL); the last three lanes contain cia5-C samples from the experiment presented in Fig. 5b (pre-acclimated in the dark). Above the immunoblot shown is the quantification of CIA5-FLAG protein accumulation (calculated as FLAG /ATPB ratio). Representative dataset of experiment repeated three times. b Immunoblot analyses of LHCSR3, CIA5-FLAG and ATPB (loading control) from whole cell extracts of WT, cia5 and four cia5-C complemented lines after exposure at 300 µmol photons m -2 s -1 for 4 hours. Among the transformants analyzed the cia5-C-a1 (cia5-C throughout the text) was retained for further analyses in the present study. Representative dataset of experiment repeated three times. c A total of 24, 12 and 6 x 10 3 cells of WT, cia5 and cia5-C-a1 were spotted on high-salt media agar plates and grown under 100 µE m -2 s -1 for four days.

pre-acclimation in LL dark
Supplementary Table 3. Relative contribution of reaction flux to production of CO2. The set of reactions with non-zero contribution is the same for all strains and conditions.